The MolTrans framework learns to predict DTI as follows.
Given the input drug and protein data, a frequent consecutive sub-sequence mining module first decomposes them into a set of explicit sequences of sub-structures using a specialized decomposition algorithm with inputs consisting of vast unlabelled data. The outputs are then fed into a augmented transformer embedding module to obtain an augmented contextual embedding for each sub-structure through transformer encoders [1]}. Next, in the interaction prediction module, drug sub-structures are paired with protein sub-structures with pairwise interaction scores. A CNN layer is later applied on the interaction map to capture higher-order interactions. Finally, a decoder module outputs a score indicating the probability of pairwise interactions.
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